MarketCrunch AI - Your Personal Quant Analyst for Trading.
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Most “AI stock research” tools are dressed-up ChatGPTs, or worse, hallucinating bots. At MarketCrunch AI, we built a deep-learning quantitative AI model that analyzes 300 million+ points daily - including macro, price action, news - to give you 1-click next-day + weekly price targets. Our AI shows its work: each price target has confidence markers, backtest, and clear drivers to help you can decide on trade / size / skip. Our fans say, its Bloomberg terminal for Robinhood users.
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Hi! I'm founder & CEO of MarketCrunch AI and @ashim_datta2 (CTO) and I are super excited to share an update.
Most “AI stock research tools” feel like screeners, or worse hallucinating bots!
We built MarketCrunch AI ground up using a proprietary Deep AI model to crunch large numerical set. Not your run-of-the-mill LLM/language model, which we believe can't reliably process numbers or worse hallunicates!
We help you be more disciplined traders with: next-day + weekly price targets with confidence, backtest context, and a clear explanation, so you can decide on trade / size / skip.
Who it’s for: Pro retail traders and/accredited investors to complement their research with unique quant-based insights.
What you’ll see inside:
✅ Next-day + weekly price targets with hit-rate, backtest, technicals, and confidence level - in plain English
✅ Suggested Options based on our price target and a tool to explore strike-price + days-to-expiry
✅ AI-picks published ~5PM PT everyday by our model (no human review)
✅ Pulse: scans 2000 stocks across scores of indicators during Market open so you don't have to.
✅ Free emails alerting for 'Breakouts' with option to add watchlist and alerts.
What I’d love feedback on:
❓ Does the confidence + evidence make it easier to act responsibly (or to skip a trade)?
❓ What’s unclear, missing, or feels like “black box” hand-waving?
Note: This is research tooling, not investment advice. If you reply with a ticker you follow + your time horizon, I’ll help interpret what you’re seeing.
@ashim_datta2 @bhushan_s When do you plan to cover data beyond US stocks
@ashim_datta2 @ankit_dhadda very excited about India! Also talked to NSE and working out to launch there soon.
Tried MarketCrunch AI on $AMZN. What clicked: it doesn't just spit a number—there's confidence + context so I can decide 'trade / size / skip.' Curious how you think about calibration over time
@shaurya_prakaash — sharing two posts on how we think about confidence and calibration:
https://marketcrunch.ai/blog/stock-price-forecasts-approaching-uncertainty-with-deep-ml
Note: confidence doesn’t automatically mean “accurate.” In our analysis reports it mostly reflects how tight the forecast range is (i.e., uncertainty)—kind of like “20% chance of rain” means rain is possible, but uncertain. That said, we show in the blog post that forecasts tend to be more accurate at the very highest confidence levels.
https://marketcrunch.ai/blog/many-models-one-signal-how-ensemble-calibration-improves-stock-price-estimates
We use ensemble calibration to keep learning from past errors, so as we make more predictions, the system improves over time.
@shaurya_prakaash Appreciate you trying it on $AMZN! We calibrate per prediction using simulations and over time track errors to self-adjust.
Love the focus on explainability and confidence tags, that’s a refreshing differentiator in the AI finance space. Curious how you’re thinking about evolving confidence as markets change over time, and whether users can tune risk thresholds to match different trading styles. Would love to hear your perspective.
@shilpa_akunuri - Thanks Shilpa — appreciate that. On “confidence evolving”: our confidence tags are driven by Monte Carlo simulations (we run the model many times and measure how stable the prediction is). When markets get noisier / regimes shift, you’ll typically see wider dispersion leading to lower confidence, because the model’s outputs vary more across runs. We also monitor this over time and recalibrate / retrain so the confidence remains interpretable.
On risk thresholds: we don’t hard-code “one size fits all” rules. Instead we surface the full set of metrics (prediction, confidence/uncertainty, relevant context) and encourage users to apply their own judgment based on their risk appetite.
I’m usually skeptical of AI forecasts because they’re marketed like guarantees. I like that you’re framing it as uncertainty + scenarios. What guardrails do you have to prevent people from over-trusting the target?
@preethika_rangamgari You’re 100% right to be skeptical. Today we emphasize ‘target ≠ promise’ and show confidence/context; we’re also exploring onboarding checkpoints + clearer ‘when to skip’ cues. If we added one hard guardrail, would you prefer: max-position guidance, confidence thresholds, or a ‘what changed since yesterday’ diff?
Based on the blog posts you are doing garch + isotonic + xgboost. And i believe you have started to look at LSTM. The former is not deep neural networks. I am not saying its a bad model stack, but do you see it as deep AI tech?
@satya_chilukuri - Great question Satya. You’re right. GARCH / isotonic / XGBoost aren’t deep nets. That’s our calibration layer (post-processing).
The core forecast comes from an ensemble of deep models (~45M params across feed-forward NNs + LSTMs). After they predict, we use GARCH + isotonic + tree-based calibration to learn from past errors and make the output more reliable / interpretable across regimes.
So it’s deep learning for the signal + classical ML for trust & stability.
Super excited to see this go live ! I like the fact is just not throwing a wild guess number but goes into the details of providing confidence guardrails so I can factor those signals into my trading decision.
@amit_chopra1 exactly! We don't want to give false confidence, but provide relevant supporting data to help you trade/size/skip. Here's a blog where we explained an explore options strategy.
Also, others who's passing by, this separate blog talks about how to assess the prediction.
Backtests are where most tools get hand-wavy. What evidence do you show so users can judge if this is overfit vs legit?
@islam_toba Totally fair. We’re opinionated about showing receipts without fake certainty. If you’re evaluating rigor, what’s your minimum bar: walk-forward tests, out-of-sample windows, regime splits, or a live/paper tracker that updates daily?
How should I interpret the confidence score in practice? Like, does ‘high’ mean trade bigger, trade more often, or just ‘trust it more’?
@kanishk_agarwal6101 Short answer, no. Confidence ≠ Accuracy. It measures how tight the model's forecast range is ("uncertainty"), and not whether it will be right. Like "20% chance of raining 1 inch" doesn't mean 0.2″ rain; it means rain is possible, but uncertain.